Knots timber detection and classification with C-Support Vector Machine

Timber knots recognition is of prime importance to further determine the timber grade. The recognition is normally based on the human expert’s eyes in which can lead to some flaws based on human limitations and weaknesses. The use of X-ray can cause emits radiation and can be dangerous to the workers. This paper addresses the employment of computational methods for knot detection. A pre-processing and feature extraction methods include contrast stretching, median blur and thresholding, gray scale and local binary pattern were used. More than 400 datasets of knot images of the tropical timbers, namely Acacia and Hevea Brasiliensis have been tested using C-support vector machine as a knot classifier. The findings demonstrate different performances for three types of kernel. Linear kernel function outperformed both radial basis function and polynomial kernel functions for Acacia and Hevea Brasiliensis species. Both species classifications using linear kernel have managed to achieve a promising accuracy. Knots classification with the used of support vector machine has shown a promising result to improve the classifier and test with different types of tropical timbers

Clustering heterogeneous categorical data using enhanced mini batch K-means with entropy distance measure

Clustering methods in data mining aim to group a set of patterns based on their similarity. In a data survey, heterogeneous information is established with various types of data scales like nominal, ordinal, binary, and Likert scales. A lack of treatment of heterogeneous data and information leads to loss of information and scanty decision-making. Although many similarity measures have been established, solutions for heterogeneous data in clustering are still lacking. The recent entropy distance measure seems to provide good results for the heterogeneous categorical data. However, it requires many experiments and evaluations. This article presents a proposed framework for heterogeneous categorical data solution using a mini batch k-means with entropy measure (MBKEM) which is to investigate the effectiveness of similarity measure in clustering method using heterogeneous categorical data. Secondary data from a public survey was used. The findings demonstrate the proposed framework has improved the clustering’s quality. MBKEM outperformed other clustering algorithms with the accuracy at 0.88, v-measure (VM) at 0.82, adjusted rand index (ARI) at 0.87, and Fowlkes-Mallow’s index (FMI) at 0.94. It is observed that the average minimum elapsed time-varying for cluster generation, k at 0.26 s. In the future, the proposed solution would be beneficial for improving the quality of clustering for heterogeneous categorical data problems in many domains

Evacuation routing optimizer (EROP) / Azlinah Mohamed … [et_al.]

This report presents the solution to the two of the most critical processes in planning for flash Hood evacuation: the evacuation vehicle assignment problem (EVAP) and the evacuation vehicle routing problem (EVRP). With these solutions, the evacuation routing optimizer (EROP) is constructed. The EVAP is firstly solved, followed by the EVRP. For EVAP, discrete particle position is proposed to support the implementation of discrete particle swarm optimization called myDPSOVAP-A. Particle positions are initially calculated based on the average passenger capacity of each evacuation vehicle. We experiment with different numbers of the potential flooded areas (PFA) using two types of sequences for vehicle capacity; random and sort ascending order. Both of these sequences are tested with different inertia weights, constriction coefficients (CF), and acceleration coefficients. We analyse the performance of each vehicle allocation in four experiment categories: myDPSOVAP-A using inertia weight with random vehicle capacity, myDPSOVAP-A using inertia weight with sort ascending order of vehicle capacity; myDPSOVAP-A using CF with random vehicle capacity, and myDPSOVAP-A using CF with sort ascending of vehicle capacity. Flash flood evacuation datasets from Malaysia are used in the experiment. myDPSOVAP-A using inertia weight with random capacity was found to give the best results for both random and sort ascending order of vehicle capacity. Solutions reached by analyses with CF random and inertia weight sorted in ascending order were shown to be competitive with those obtained using inertia weight with random capacity. Overall, myDPSOVAP-A outperformed both a genetic algorithm with random vehicle capacity and a genetic algorithm with sort ascending order of vehicle capacity in solving the EVAP. Consequently EVRP, myDPSOVRPl is modified and named as myDPSO_VRP_2, adopts a new solution mapping which incorporates a graph decomposition and random selection of priority value. The purpose of this mapping is to reduce the searching space of the particles, leading to a better solution. Computational experiments involve EVRP dataset from road network for flash flood evacuation in Johor State, Malaysia. The myDPSOVRPl and myDPSO_VRP_2 are respectively compared with a genetic algorithm (GA) using solution mapping for EVRP. The results indicate that the proposed myDPSO_VRP_2 are highly competitive and show good performance in both fitness value and processing time. Overall, DPSOVRP2 and myDPSOVAP-A which are the main component in the EROP gave good performance in maximizing the number of people to vehicles and minimizing the total travelling time from vehicle location to PFA. EROP was embedded with the DPSOVRP2 and retrieved the generated capacitated vehicles from the myDPSOVAP-A. EROP is also accommodated with the routing of vehicles from PFA to relief centres to support the whole processes of the evacuation route planning

The potential use of augmented reality in gamification

The use of augmented reality (AR) and gamification in various fields is currently gaining popularity for its capability in engaging users.Gamification is a term that defines the use of game-based elements, such as game mechanics, aesthetics, dynamics, and game thinking in the non-game context environment.Meanwhile, AR is a technology that has an ability to overlap computer graphics onto the real environment.However, as a newly emerging concept, gamification seems to have some arguments related to its elements, concepts, and effectiveness in a similar intervention. Therefore, this paper discusses gamification. Although dozens of studies have implemented AR games, there is still an obvious lack in discussing and relating it to a gamified platform. Nevertheless, previous adaptation of games in AR seems that there are also potentials to utilise gamification and AR concepts and elements, as well as AR and AR games in brief.This paper also justifies several previous empirical studies in AR and gamification to look into its elements, research design, and the potentials of AR and gamification combinatio

Hybrid filtering methods for feature selection in high-dimensional cancer data

Statisticians in both academia and industry have encountered problems with high-dimensional data. The rapid feature increase has caused the feature count to outstrip the instance count. There are several established methods when selecting features from massive amounts of breast cancer data. Even so, overfitting continues to be a problem. The challenge of choosing important features with minimum loss in a different sample size is another area with room for development. As a result, the feature selection technique is crucial for dealing with high-dimensional data classification issues. This paper proposed a new architecture for high-dimensional breast cancer data using filtering techniques and a logistic regression model. Essential features are filtered out using a combination of hybrid chi–square and hybrid information gain (hybrid IG) with logistic regression as classifier. The results showed that hybrid IG performed the best for high-dimensional breast and prostate cancer data. The top 50 and 22 features outperformed the other configurations, with the highest classification accuracies of 86.96% and 82.61%, respectively, after integrating the hybrid information gain and logistic function (hybrid IG+LR) with a sample size of 75. In the future, multiclass classification of multidimensional medical data to be evaluated using data from a different domain

Study of stereochemical effect of galactoside mixture based on reaction time / Nurul Fadhilah Kamalul Aripin, Mohamad Iqbal Ab Doroh and Marina Yusoff

Synthesis of palm kernel oil-based galactoside involves a three-step process: acetylation, glycosylation and deacetylation. Glycosylation reaction produced two stereoisomeric galactosides: α-anomer and β-anomer. The quantity of these anomers is directly dependent on the glycosylation time as each anomer is either kinetically or thermodynamically favoured. In this work, we studied the effect of time towards the production of predominating anomers. The quantity of the anomers is reported in terms of α/β ratio based on the integration of proton NMR peaks of the final product. From the result, it is found that the quantity of α-anomer increased by average of 1.15 %/

Survey on highly imbalanced multi-class data

Machine learning technology has a massive impact on society because it offers solutions to solve many complicated problems like classification, clustering analysis, and predictions, especially during the COVID-19 pandemic. Data distribution in machine learning has been an essential aspect in providing unbiased solutions. From the earliest literatures published on highly imbalanced data until recently, machine learning research has focused mostly on binary classification data problems. Research on highly imbalanced multi-class data is still greatly unexplored when the need for better analysis and predictions in handling Big Data is required. This study focuses on reviews related to the models or techniques in handling highly imbalanced multi-class data, along with their strengths and weaknesses and related domains. Furthermore, the paper uses the statistical method to explore a case study with a severely imbalanced dataset. This article aims to (1) understand the trend of highly imbalanced multi-class data through analysis of related literatures; (2) analyze the previous and current methods of handling highly imbalanced multi-class data; (3) construct a framework of highly imbalanced multi-class data. The chosen highly imbalanced multi-class dataset analysis will also be performed and adapted to the current methods or techniques in machine learning, followed by discussions on open challenges and the future direction of highly imbalanced multi-class data. Finally, for highly imbalanced multi-class data, this paper presents a novel framework. We hope this research can provide insights on the potential development of better methods or techniques to handle and manipulate highly imbalanced multi-class data

Glycolipids from Natural Sources : Dry Liquid Crystal Properties, Hydrogen Bonding and Molecular Mobility of Palm Kernel Oil Mannosides

Acknowledgement This work was supported by the Ministry of Education of Malaysia [600-RMI/RAGS 5/3 (140/2014), 2014]; University Malaya [RP038B-17AFR, 2017]; Royal Academy of Engineering, U.K., and Academy of Science, Malaysia [NRCP1516/4/61, 2016] and the University of Aberdeen [SF10192, 2018]. AMF and TS would like to acknowledge the financial support provided by University Malaya for the Visiting Lecturer fellowshipPeer reviewedPostprin

Intestinal colonization against Vibrio cholerae: host and microbial resistance mechanisms

Vibrio cholerae is a non-invasive enteric pathogen known to cause a major public health problem called cholera. The pathogen inhabits the aquatic environment while outside the human host, it is transmitted into the host easily through ingesting contaminated food and water containing the vibrios, thus causing diarrhoea and vomiting. V. cholerae must resist several layers of colonization resistance mechanisms derived from the host or the gut commensals to successfully survive, grow, and colonize the distal intestinal epithelium, thus causing an infection. The colonization resistance mechanisms derived from the host are not specific to V. cholerae but to all invading pathogens. However, some of the gut commensal-derived colonization resistance may be more specific to the pathogen, making it more challenging to overcome. Consequently, the pathogen has evolved well-coordinated mechanisms that sense and utilize the anti-colonization factors to modulate events that promote its survival and colonization in the gut. This review is aimed at discussing how V. cholerae interacts and resists both host- and microbe-specific colonization resistance mechanisms to cause infection

Review : Machine and deep learning methods in Malaysia for COVID-19

The global pandemic of the coronavirus disease COVID-19 has impacted a variety of operations. This dilemma is also attributable to the lockdown measures taken by the afflicted nations. The entire or partial shutdown enacted by nations across the globe affected the majority of hospitals and clinics until the pandemic was contained. The judgements made by the authorities of each impacted nation vary based on a number of variables, including the nation's severity of reported cases, the availability of vaccines, beds in intensive care unit (ICU), staff number, patient number, and medicines. Consequently, this work offers a thorough analysis of the most recent machine learning (ML) and deep learning (DL) approaches for COVID-19 that can assist the medical field in offering quick and exact COVID-19 diagnosis in Malaysia. This research aims to review the machine learning and deep learning methods that were used to help diagnose COVID-19 in Malaysia